Dataset Open Access

The growth of COVID-19 scientific literature: A forecast analysis of different daily time series in specific settings

Torres-Salinas, Daniel; Robinson-García, Nicolás; van Schalkwyk, François; Nane, Gabriela F.; Castillo-Valdivieso, Pedro

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  <identifier identifierType="DOI">10.5281/zenodo.4478251</identifier>
      <creatorName>Torres-Salinas, Daniel</creatorName>
      <affiliation>Universidad de Granada</affiliation>
      <creatorName>Robinson-García, Nicolás</creatorName>
      <affiliation>Universidad de Granada</affiliation>
      <creatorName>van Schalkwyk, François</creatorName>
      <familyName>van Schalkwyk</familyName>
      <affiliation>Stellenbosch University</affiliation>
      <creatorName>Nane, Gabriela F.</creatorName>
      <givenName>Gabriela F.</givenName>
      <affiliation>TU Delft</affiliation>
      <creatorName>Castillo-Valdivieso, Pedro</creatorName>
      <affiliation>Universidad de Granada</affiliation>
    <title>The growth of COVID-19 scientific literature: A forecast analysis of different daily time series in specific settings</title>
    <date dateType="Issued">2021-01-29</date>
  <resourceType resourceTypeGeneral="Dataset"/>
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    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.4478250</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;Submitted to&amp;nbsp;The ISSI 2021 Conference.&amp;nbsp;The conference is organised by KU Leuven in close collaboration with the university of Antwerp under the auspices of ISSI &amp;ndash; the International Society for Informetrics and Scientometrics (&lt;a href=""&gt;;/a&gt;).&amp;nbsp;&lt;/p&gt;

&lt;p&gt;We present a forecasting analysis on the growth of scientific literature related to COVID-19 expected for 2021. Considering the paramount scientific and financial efforts made by the research community to find solutions to end the COVID-19 pandemic, an unprecedented volume of scientific outputs is being produced. This questions the capacity of scientists, politicians and citizens to maintain infrastructure, digest content and take scientifically informed decisions. A crucial aspect is to make predictions to prepare for such a large corpus of scientific literature. Here we base our predictions on the ARIMA model and use two different data sources: the Dimensions and World Health Organization COVID-19 databases. These two sources have the particularity of including in the metadata information on the date in which papers were indexed.&amp;nbsp; We present global predictions, plus predictions in three specific settings: by type of access (Open Access), by NLM source (PubMed and PMC), and by domain-specific repository (SSRN and MedRxiv). We conclude by discussing our findings.&lt;/p&gt;</description>
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